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Convolutional networks beyond the Euclidean space
Convolutional networks have revolutionized image analysis and data science. They are among the main driving forces behind the current AI revolution. This presentation outlines key developments, beginning with the initial breakthrough in image classification (Krizhevsky et al., NeurIPS 2012), and then explores efforts to extend the capabilities of deep networks to broader challenges in image analysis, shape analysis, medicine, and the life sciences. Examples include optical flow estimation (Dosovitskiy et al., ICCV 2015), the acceleration of diffusion tensor imaging (Golkov et al. 2016), tuberculosis screening from chest x-rays (Golkov et al., Sci. Reports 2019), and protein structure prediction (Golkov et al., NeurIPS 2016). It also includes a brief overview of convolutional networks for graph-valued data and introduces a recent solution that extends these networks to directed graphs using holomorphic functional calculus (Koke & Cremers, ICLR 2024).
Session Objectives:
By the end of the session, participants will be able to:
Explain the fundamental principles and architecture of convolutional networks.
Apply convolutional networks to non-Euclidean data structures.
Explore real-world applications of convolutional networks in medical imaging and the life sciences.
Speakers:
Daniel Cremers
Professor, Technical University of Munich
Moderators:
Arnout Devos
Scientific Coordinator, European Laboratory for Learning and Intelligent Systems (ELLIS)
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Disclaimer:
The views and opinions expressed are those of the panelists and do not reflect the official policy of the ITU.
